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Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Engy N. Eltayeb, Nancy M. Salem, Walid Al-Atabany
10.5120/ijca2018917008

Engy N Eltayeb, Nancy M Salem and Walid Al-Atabany. Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images. International Journal of Computer Applications 180(38):1-7, May 2018. BibTeX

@article{10.5120/ijca2018917008,
	author = {Engy N. Eltayeb and Nancy M. Salem and Walid Al-Atabany},
	title = {Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2018},
	volume = {180},
	number = {38},
	month = {May},
	year = {2018},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume180/number38/29376-2018917008},
	doi = {10.5120/ijca2018917008},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper, a study for evaluating the efficacy of different feature sets that used brain tumor classification is presented. Different features sets are extracted as shape, 1st order texture features (FOS), 2nd order (GLCM, GLRLM), boundary features, and wavelet-based features. The brain tumors are extracted using the k-means clustering algorithm. Then different classifiers such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used in the classification process.

A set of 65 real and simulated (Flair modality) MRI images from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge is used for performance evaluation. The overall segmentation results for the 65 volumes are 90.15±0.12. For the Feature sets efficacy step, the highest accuracy of 94.74% is achieved by the SVM when using the wavelet–based features. The lowest accuracy achieved by the three classifiers obtained when using the second order texture features..

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Keywords

Brain tumor segmentation, Feature extraction, Wavelet Transform.